Abstract
In our real life, there is much hybrid data which contains not only unstructured data but also structured data. In general, the majority techniques of text search on hybrid data are only focused on unstructured data (text) ignoring the structured data. So this may lead a bad ranking of the searching results. In this paper, we describe a new method about improving text search using structured data. Our contributions are summarized as follows: (i) We build the uniform problem model; (ii) Ours is the first approach adopting the mutual information of feature words to qualify the relevance (similarity) between two texts; and (iii) We utilize several rules to consider the structured data to improve text search and build our approach. Finally, experimental results show the relevance function and our approach guarantees the search results with high recall, top-k precision, Mean Average Precision and good search performance, respectively.
The work is partially supported by the National Natural Science Foundation of China (Nos. 60973018), the National Natural Science Foundation of China (No. 60973020), the Doctoral Fund of Ministry of Education of China (No. 20110042110028) and the Fundamental Research Funds for the Central Universities (Nos. N110804002, N110404015).
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Zhu, H., Yang, X., Wang, B., Wang, Y. (2012). Improving Text Search on Hybrid Data. In: Bao, Z., et al. Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33050-6_20
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DOI: https://doi.org/10.1007/978-3-642-33050-6_20
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